Bringing nature back: using hibernation to reboot organ preservation
Bibliographic record
Abstract
Recently, organ transplant therapy has received a major boost from a change in perspective - a move away from damaging, cold static organ storage to the use of warm normothermic perfusion. The concept for warm preservation is one that has been borrowed from Nature, and it is only fitting that we go back to the wild for more 'tricks' to further improve warm organ stabilization. Current warm preservation strategies are designed to mimic natural conditions in the human body as closely as possible, but what if we could mimic these conditions while simultaneously inducing a reversible state of torpor that would further extend the viability window of donor organs? Indeed, the original driver for using cold organ storage was its ability to strongly reduce metabolic rate many-fold when organs were cooled from 37 to 5 °C. Herein, we discuss the adaptations that allow warm hibernators such as bears and lemurs (fellow primates) to naturally depress their metabolic rate and retreat into states of suspended animation, and how these can be applied to improve organ transplant therapy. Can we look to Nature for instructions to induce torpor in human organs? This article discusses the possibilities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".